Statistik

Leverage Effect for Volatility with Generalized Laplace Error by Farrukh Javed(
)1
edition published
in
2014
in
English
and held by
1 WorldCat member
library
worldwide
We propose a new model that accounts for the asymmetric response of volatility to positive (`good news') and negative (`bad
news') shocks in economic time series – the so-called leverage effect. In the past, asymmetric powers of errors in the conditionally
heteroskedastic models have been used to capture this effect. Our model is using the gamma difference representation of the
generalized Laplace distributions that efficiently models the asymmetry. It has one additional natural parameter, the shape,
that is used instead of power in the asymmetric power models to capture the strength of a long-lasting effect of shocks. Some
fundamental properties of the model are provided including the formula for covariances and an explicit form for the conditional
distribution of `bad' and `good' news processes given the past – the property that is important for the statistical fitting
of the model. Relevant features of volatility models are illustrated using S&P 500 historical data

Higher order moments of the estimated tangency portfolio weights by Farrukh Javed(
)1
edition published
in
2017
in
English
and held by
1 WorldCat member
library
worldwide
In this paper we consider the estimated tangency portfolio weights. We derive analytical expressions for the higher central
and non-central moments of these weights. The main focus has been given to skewness and kurtosis due to the importance of
asymmetry and heavy tails of the data. We complement our results with an empirical study where we analyze an international
diversified portfolio

Estimation of solar energy potential for Islamabad, Pakistan by Terragreen 2012: Clean Energy Solutions for Sustainable Environment (CESSE)(
)1
edition published
in
2012
in
English
and held by
1 WorldCat member
library
worldwide
In order to design a solar energy system with optimized performance a thorough knowledge of solar radiation data for a considerably
long period (20-25 years) is a pre-requisite. For developing countries like Pakistan, the need of empirical models to assess
the feasibility of solar energy utilization seems inevitable due to the absence and scarcity of trustworthy solar radiation
data. We present such models for the capital city of Pakistan, Islamabad to estimate global and diffuse solar radiation. It
is found that with the exception of monsoon month, solar energy can be utilized very efficiently throughout the year. The
models suggested could be used for most of the north-eastern areas of Pakistan, which are similar to Islamabad with respect
to the climate and the availability of solar radiation but lack in the record of solar radiation data

Conditional Two Level Mixture with Known Mixing Proportions Applications to School and Student Level Overweight and Obesity
Data from Birmingham, England by Ghazi Shukur(
)1
edition published
in
2014
in
English
and held by
1 WorldCat member
library
worldwide
Two Level (TL) models allow the total variation in the outcome to be decomposed as level one and level two or ‘individual
and group’ variance components. Two Level Mixture (TLM) models can be used to explore unobserved heterogeneity that represents
different qualitative relationships in the outcome. In this paper, we extend the standard TL model by introducing constraints
to guide the TLM algorithm towards a more appropriate data partitioning. Our constraints-based methods combine the mixing
proportions estimated by parametric Expectation Maximization (EM) of the outcome and the random component from the TL model.
This forms new two level mixing conditional (TLMc) approach by means of prior information. The new framework advantages are:
1. avoiding trial and error tactic used by TLM for choosing the best BIC (Bayesian Information Criterion), 2. permitting meaningful
parameter estimates for distinct classes in the coefficient space and finally 3. allowing smaller residual variances. We show
the benefit of our method using overweight and obesity from Body Mass Index (BMI) for students in year 6. We apply these methods
on hierarchical BMI data to estimate student multiple deprivation and school Club effects